Method and system for generating an index of investment returns

ABSTRACT

A method and system for generating returns for investments in asset classes such as bonds, currencies, and commodities. The index of these returns may be used as a benchmark to measure the investment performance of one or more of the asset classes that make up the index. It may also be replicated in the markets in which futures contracts for members of these asset classes are traded and used to earn the returns that the index measures. Indices constructed for each asset class can be combined with each other and with equity indices to create tradable indices hedge fund returns.

[0001] This application claims priority from U.S. Provisionalapplication Serial No. 60/202,790 Filed May 9, 2000 which is herebyincorporated herein by reference.

FIELD OF THE INVENTION

[0002] The invention relates to a method for measuring and earning thefundamental returns to investing in asset classes including non-equityasset classes.

BACKGROUND OF THE INVENTION

[0003] More than one hundred billion dollars (US) is currently investedin hedge funds, private investment funds with broad mandates and powers,including the ability to use leverage, take short positions and chargeperformance-related fees. The popularity of such funds is driven in partby their investment flexibility and by a desire for diversification onthe part of investors. As a group, hedge funds invest in a wide varietyof asset classes, including world equity (stock) markets and commercial(non-equity) asset classes such as global bonds, currencies andcommodities. (An asset class is simply a set of similar assets such asstocks, bonds, currencies, and commodities, including all securities orcontracts based on the assets such as futures and forward contracts.)While investment strategies used in equity markets overwhelminglyinvolve buying individual stocks or groups of stocks, the investmentstrategies used in these other asset classes are said to be“opportunistic” in the sense that positions are much more likely to varyover time in both size and direction. For example, the funds may havelong bond positions when interest rates are expected to decline, andshort bond positions when interest rates are expected to rise. (A longposition refers to a purchase, or agreement to purchase, a particularasset, while a short position refers to a sale or agreement to sell.)Since the returns from investing in bonds, currencies and commoditiestend to be uncorrelated with equity returns, the varied nature of hedgefunds' investment profile has great appeal for investors in search ofdiversification beyond traditional assets.

[0004] A major obstacle to further growth in hedge fund investments,particularly for institutions, is the lack of performance benchmarks.Unlike equity funds, for example, where a number of equity indices areavailable for use in evaluating performance, hedge funds operate in abenchmark vacuum. Ironically, it is the same asset categories that arethe source of so much diversification that pose the difficulties todeveloping a valid benchmark. The common view in financial circles isthat the dynamic nature of bond, currency and commodity investments(sometimes long, sometimes short) present difficulties for indices thatare insurmountable.

[0005] Nobel Laureate William F. Sharpe proposed in a 1992 article,Asset Allocation: Management Style and Performance Measurement (Journalof Portfolio Management, Winter 1992), that the returns of mutual fundscould be explained by a linear combination of a small number of factors.Sharpe was concerned with mutual funds that invested in traditionalasset classes, namely, stocks and bonds, and did not use leverage ortake short positions. The explanatory factors he uncovered were thetraditional investment benchmarks such as the S&P 500, or indices ofsmall capitalization stocks or growth stocks. Critically, each of thesebenchmarks is based on market prices of the securities included in thebenchmark.

[0006] Sharpe's article was the genesis of “style analysis,” the attemptto categorize and better evaluate the performance of differentinvestment managers. Other authors have attempted to extend styleanalysis beyond managers who invest in equities to those who invest inasset classes such as global bonds, currencies, and commodities, an areacommonly known as “alternative investments.” This is the domain of hedgefunds and commodity trading advisors (CTAs).

[0007] Application of Sharpe's method to alternative investment managersis hampered by the nature of the investment activity. Hedge fundmanagers and CTA's typically take both long and short positions in themarkets in which they trade, so direct application of buy and holdbenchmarks cannot capture their investment returns. As a consequence,attempts to benchmark the performance of hedge fund managers and CTA'shave tended to degenerate into indices that combine the investmentreturns of similar managers, called “manager benchmarks.” These indicessimply bypass the requirement that a useful benchmark be based directlyon market prices.

[0008] There is a need for a system that provides benchmarks based onmarket prices for asset classes other than equity. The present inventionsatisfies this and other needs.

SUMMARY OF THE INVENTION

[0009] The present invention is a method and system for generating aseries of returns to investing in asset classes such as bonds,currencies and commodities. Unlike equities, the intrinsic, orfundamental, returns to investing in these categories of assets can onlybe captured by investment strategies that take short positions as wellas long positions at appropriate times. We call such asset classes“commercial,” and the markets in which particular members of theseclasses trade, “commercial markets.” Almost all markets except equity(stock) markets are commercial in this sense.

[0010] The reason that capturing the intrinsic returns to investing incommercial markets requires investment strategies that can be short aswell as long is that the participants in commercial markets includenatural “hedgers.” These participants use these markets to hedge, orpartially offset, risks that arise in the normal course of theirbusiness. For example, wheat producers risk the possibility that marketprices for wheat will be lower when their wheat is harvested, but canuse short positions in wheat futures to offset this risk to greater orlesser extent. Bakers, on the other hand, risk the possibility thatflour prices will rise in the future, and can offset this risk throughlong positions in wheat futures. Investors earn returns from investingin wheat futures because they bear the risks that these importantcommercial interests want to reduce, and earning these returns requiresthat investors have both long and short positions at different times.

[0011] Upon selection of the commercial asset classes to which it is tobe applied, in accordance with the present invention an index forinvestment returns may be generated having at least two primaryfeatures. First, the index may be used as a benchmark to measure theperformance of the asset class comprising the index. The benchmarkindicates the intrinsic returns to investing in any reasonablyrepresentative group of assets in that class. Second, the index may bereplicated in the markets in which these assets are traded. Such atradable index can be used to earn the fundamental returns that theindex measures. It thus can form the basis of an index-based investmentfund, or index fund

[0012] Indices generated by application of the present invention are notlimited to homogeneous collections of assets. In contrast to mostindices currently used by investors that focus on assets of a particularnature (for example, stock market indices include only stocks,commodities indices include only commodities), indices generated withthe present invention may combine many different classes of assets.Indices constructed for each of these asset classes can be combined notonly with each other but the individual indices, or any group of them,can also be combined with existing equity indices. For example,currencies, commodities, and bonds are three asset classes that may berepresented in a single index by combining indices constructed for eachof these asset classes. The resulting index can be combined with anindex generated in the future markets for equities, e.g., a futuresindex for the S&P 500, to produce a composite index representing thereturns of all of the major assets classes.

[0013] In accordance with the preferred embodiment of the presentinvention, an index for any commercial asset class can be generated frommarket prices for futures and forward contracts for representativemembers of the class. First, representative assets and futures contractson these assets are selected for each of the asset classes. In case ofcurrencies, for example, consider the British Pound, Japanese Yen, SwissFranc, Australian Dollar, Canadian Dollar, and Euro (all expressedrelative to the US Dollar), and the futures contracts for thesecurrencies that are traded on the IMM division of the Chicago MercantileExchange, with deliveries in March, June, September and December.

[0014] Second, calculate indices for each commercial asset class, hencea global currency index, a global bond index, and a commodity index.Each of these indices may be calculated by applying the algorithmsdescribed below to the market prices of representative futures contractsfor each asset class. For example, the MLM Index™ algorithm (describedbelow) may be used, except that a separate index is created for eachcommercial asset class.

[0015] Finally, the indices for the different commercial asset classescan be combined with each other and/or with existing stock marketindices such as the S&P 500. In combining indices, a weight is assignedto each component index where, for example, the weight represents theproportion of each dollar invested in the overall index to be allocatedto each component index. The resulting index return is the weightedaverage of the returns of each component index. There are an infinitenumber of weighting combinations that can be determined in a variety ofways. Moreover, the weights do not have to add to one. In fact, if thesum of the weights exceed one, it means that the portfolio employsleverage, and the extent to which the sum of the weights exceeds onedetermines the degree of leverage.

[0016] The present invention has broad applicability. One of itsbroadest applications is a tradable index (or performance benchmark) ofhedge fund returns. Hedge funds have been characterized as investmentfunds that make leveraged bets on anticipated price movements of stockmarkets, interest rates, foreign exchange, and physical commodities. Forexample the MLM GMS™, an index embodying the present invention, combinesin a particular way individual indices for global bonds, globalcurrencies, and commodities with the major stock market indices, e.g.,S&P 500 (Standard and Poors 500 stock index, US), CAC 40 (Compagnie desAgents de Change-40, France), DAX (deutsche Aktienindex, Germany),FTSE-100 (Financial Times Stock Exchange 100 stock index, UK), Nikkei225 (Nihon Keizai Shimbun, Japan). The returns of this tradeable indexclosely track the returns of hedge funds generally and “global macro”hedge funds in particular.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The attached figures show schematically how indices can beconstructed for any asset class or for combinations of different assetclasses using the present invention.

[0018]FIG. 1 is a flow chart showing the method of generating an indexfor any asset class according to the preferred embodiment of the presentinvention;

[0019]FIG. 2 is a flow chart showing the method of generating an indexfor any combination of asset classes, including those representative ofhedge funds, according to the preferred embodiment; and

[0020]FIG. 3 is block diagram showing the relationship among variousterms employed.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0021] In the preferred embodiment of the present invention, an indexmay be generated from market prices for futures and forward contractsfor a representative sample of assets in any commercial asset class. Theindex may be used as a benchmark for evaluating the performance ofinvestment managers who invest in these asset classes. For example, theindex may be used for style analysis of hedge funds and commoditytrading advisors. The index may also be replicated directly in thefutures and forward markets from which it is derived so that investorsin the index can earn the measured return.

[0022] The preferred embodiment of the present invention expands andimproves the methodology used in the MLM Index™ (described below) toapply it to commercial asset classes individually and in variouscombinations. These applications enable the development of tradeablebenchmarks for investments in any group of these commercial assetclasses and, in combination with equity benchmarks, for the investmentperformance of hedge funds as a group (and for particular hedge fundcategories, especially “global macro” hedge funds). In constructingthese indices, the preferred embodiment of the present invention mayalso use indices other than the newly developed ones described herein tocapture the returns in some or all of the commercial asset classes, justas it uses existing indices for equity markets. However, in order to beuseful in practicing the present invention a commercial market indexmust adequately allow for both long and short investments in thesemarkets and must be capable of being replicated in these markets in realtime. Examples of “real time” indices include the S&P 500, Nikkei, theShearson Lehman bond index, and the Morgan Stanley Europe Asia Far East(EAFE). On the other hand indices that are not available in real time(i.e. those computed days or weeks after the events on which suchcomputations are based) include the Credit Suisse First Boston/Tremonthedge fund index (CSFB/Tremont) and the Zurich Trading Advisor index.

[0023] The indices that are derived from the preferred embodiment of thepresent invention have all of the characteristics and uses of anyfinancial performance benchmark. They are based on exact mathematicalcalculations that can be easily reproduced and verified. Thecalculations can be used to compare and evaluate the performance of anyinvestment managers who invest in these markets including hedge fundmanagers and CTA's. Since these funds charge investors a performancefee, the benchmarks can also be used to define appropriate “hurdlerates” on which to base such fees. For example, investors might insiston paying performance fees only to the extent that the manager'sperformance beats that of the index. Finally, for investors who preferto invest directly in benchmarks rather than in actively managed funds,these benchmarks can be efficiently traded, i.e., replicated in themarkets from which they were derived with negligible tracking error, andincluded in a portfolio.

[0024] The index methodology derives from two critical observations andconcepts: (1) that in order to capture the intrinsic returns toinvesting in most asset classes other than equities, a benchmark mustreflect the returns from short as well as long positions at differenttimes (as opposed to equity markets in which the fundamental returnscome from being long only); and (2) that these returns can be measured,and replicated, from market prices for futures and forward contracts fora representative sample of assets in any commercial asset class. Thesefundamental concepts are embodied in the algorithms presented herein.The algorithms enable one to construct indices for any commercial assetclass, or for any combination of commercial asset classes and existingstock market indices.

[0025] As shown in FIG. 1, there are several aspects to the processincorporated in the algorithms, including: (step 110) selectingrepresentative assets for an asset class and representative markets,futures contracts, and holding periods for these assets; (step 112)constructing from the different futures contracts for each asset classmember a continuous futures series for the asset to which a long/shortdecision rule can be applied in real time; (steps 114-116) specifying adecision rule and constructing returns for the representative assetsfrom application of the decision rule to the relevant contracts; (steps118-120) combining individual asset returns to create asset classindices, and (step 122) combining asset class indices to create “larger”indices such as an index for global bonds and currencies, or, togetherwith stock market indices, for hedge funds. A continuous futures seriesis used because futures (and forward) contracts expire periodicallycreating discontinuities in their price series.

[0026] The prior art MLM Index™ has been in commercial use for more than10 years, and has an established reputation in the market as a benchmarkof the returns available to futures investors. The MLM Index™ has beenaccepted by the Department of Labor as a benchmark for the payment ofincentive or performance fees for institutional futures investments. TheMLM Index™ is based on a portfolio of assets whose futures contracts areamong the most active futures contracts traded on U.S. futuresexchanges. (The group of futures contracts for the different assets aresometimes called the futures “market” for that asset; thus the futurescontracts for soybeans might be called the soybean futures market.) Themonthly rate of return of the index equals the simple average of themonthly rates of return of the markets in the Index plus the T-billrate. This index reflects long and short positions but, unlike thepreferred embodiment, it combines returns from different asset classes.

[0027] The existing MLM Index™ is based on futures markets for 25assets, namely, 6 currencies, 3 interest rates and 16 physicalcommodities. For each market, specific representative futures contractsare specified and the return calculated from positions based on the 12month moving average of a constructed continuous futures series. Forexample, to calculate the return for soybeans market, the followingsteps would be executed:

[0028] 1. Select four representative soybean futures contracts, forexample, the March, May, July, and November contracts, and calculate acontinuous futures series (CF) for these contracts.

[0029] 2. Apply the 12-month moving average filter to determine theposition. If the current value of the CF is above the average CF, thenestablish a long position for the subsequent month; otherwise take ashort position.

[0030] 3. Determine the return for the next holding period bycalculating the percentage price change, and applying the positiondirection. For example, if the soybean price increases by 10% and theposition determined at the end of the previous period was short, thenthe return would be minus 10% (−10%).

[0031] 4. Calculate the return for all 25 assets in the Index, followingthe same steps 1-3.

[0032] 5. The return for the MLM Index™ then is the average, for eachholding period, of the returns of the individual markets plus thecurrent T-bill rate.

[0033] The MLM Index™ makes no distinction between the related assets inthe index. It is a composite of the returns for 25 assets whose futurescontacts are traded in liquid futures markets on U.S. futures exchanges.As such, it combines in one index the returns from different assetclasses. It also is limited to the particular equal weighting employedin its design. Therefore, it has limited use in style analysis of hedgefunds.

[0034] As a preface to the full details of the algorithms used in thepreferred embodiment, the following is a list of terms used in thealgorithms along with definitions and abbreviations for the terms.

[0035] Asset class (C)—An asset class is a set or group of similarassets such as bonds, stocks (equities), currencies, or commodities,including the securities or contracts based on them. Specific members(m) of these asset classes, and the related securities and contracts(financial instruments), trade in markets.

[0036] Continuous futures series (CF)—A continuous series calculated fora predefined chronological list of futures contracts for a particularasset class member. In order to calculate the investment returns forthese asset class members from their futures contracts, a continuousseries must be created. In futures markets, returns cannot be computeddirectly from actual market prices of futures contracts because thosecontracts expire periodically creating large discontinuities in theactual prices. Therefore in order to establish a basis for deciding thenext position, it is necessary to create a continuous series of returnsthat could be earned by investing in a sequence of actual futurescontracts. For example, a March futures contract is purchased in Januaryfor price $5; in March the contract is sold for $6 and a June contractis purchased at price $8; there is discontinuity in the price at whichthe March contract is sold and the June contact is purchased. Usingactual prices from a sequence of contracts would misrepresent investmentresults. Instead, a continuous series is created by applying, to anarbitrary starting value, the returns from holding individual contacts.This analogous to adjusting stock prices for a stock split.

[0037] Contract (k)—Specific futures contract used in an indexcalculation. For any member of an asset class (m), contract k is aspecific futures contract, traded on a specific exchange for delivery ona specific date. Futures contracts are the preferred instrument to gaugethe returns for the preferred embodiment because they are exchangetraded and allow for variable leverage.

[0038] Filter (FL)—A mathematical rule or algorithm used to determinelong or short positions for any member in any holding period.

[0039] Holding period (h)—The period of position evaluation. Forexample, if the holding period is a calendar month, a position would beheld for one month and reevaluated at the end of the month to determinethe position for the subsequent month.

[0040] Market—The market, or group of markets, in which securities orcontracts for particular members (m) of an asset class are traded. Forexample, if the asset class is currencies, a member may be the JapaneseYen, and the corresponding market the futures market for the JapaneseYen (expressed relative to the U.S. dollar) Market Position—seeposition.

[0041] Market Price—the price of a specific exchange traded futurescontract at a specific point in time. See PR(h,k,m) and PL(h,k,m).

[0042] MLMI (h,C)—A composite index of an asset class C for holdingperiod h.

[0043] N—The number of members within an asset class.

[0044] PL(h,k,m)—The market price for futures contract k for asset classmember m on the last day of holding period h.

[0045] Position (PO)—Position is a variable which indicates whether aparticular member of an asset class is long or short (bought or sold),taking the values 1 and −1 respectively. The position is determined bythe application of the filter (FL) to the continuous futures series (CF)for that member.

[0046] PR(h,k,m)—The market price for futures contract k for asset classmember m on the next to last day of the holding period h.

[0047] Return (R)—The percentage change in the price or value of aspecific futures contract, group of contracts, or index over a holdingperiod.

[0048] R(h, m)—The return for asset class member m during holding periodh. It is calculated as the percentage change in the market price of aspecific futures contract for that member.

[0049] R(h,C)—The return of asset class C over holding period h. It isthe average of the returns for the chosen representative members of theclass.

[0050] R(h,I)—The return of index I during holding period h. It is aweighted average of the returns for the asset classes in the index. Theweights are determined separately for different indices.

[0051] Referring to FIG. 3, the members 312 of an asset class 310 arethe selection of assets of a uniform type. Each member 312 is aparticular asset. The securities and contracts based on that asset aretradable in a corresponding market 314. A market 314 may be spot 316 orfutures 318. With respect to the futures market 318, trading isperformed by way of futures contracts 320.

I. Index Construction for Any Asset Class

[0052] Referring to FIG. 1, an index for each class may be constructedby the following steps:

[0053] Step 110—selecting the representative members of the asset class,the particular contracts for each of these members, and the holdingperiod. For any commercial asset class C, define the followingparameters and variables:

[0054] a. The members to be included and the period for selection;typically the members are selected annually, on the basis of liquidityof the futures markets in which they are traded.

[0055] b. The futures or forward contracts to be used within each marketare selected periodically, generally annually. Typically these will bethe four most liquid futures contracts.

[0056] c. The holding period or length of time a position will be held.This parameter is determined separately for each asset class; it isgenerally a month.

[0057] d. The contracts used in each holding period; generally thenearest contract among those selected in step b. above which is notdeliverable (i.e., does not expire) in the subsequent holding period.

[0058] Step 112—Computing a continuous futures series. For each member,m, construct a continuous futures series as follows: For each holdingperiod h, let PR(h,k,m) be the market price of futures contract k on thenext to last day of holding period h. For example, if k=June, m=JapaneseYen and h=the calendar month ending Mar. 31, 2000, then PR is the marketprice of the June Japanese Yen contract on the next to the last businessday of the month, e.g. Mar. 30, 2000. The continuous futures series, CF,for member m in holding period h is then defined by:

CF(h,m)=CF( h−1,m)*(PR(h,k,m)/PR(h−1,k,m))  (1)

[0059] where CF(0,m) is set at an arbitrary beginning value, normally1000.

[0060] CF solves the price discontinuity problem common to all futuresmarkets that occurs when one futures contract expires and a subsequentcontract continues trading at a different price. For example, a MarchJapanese Yen contract expires in March and a subsequent contract, saythe June Japanese Yen contract, continues trading at a different price.Using the price on the next to the last business day of the holdingperiod (e.g., month) in the computation of CF allows the index to bereplicated in the markets in real time since the position is determinedusing data that are available before the price for last day isestablished.

[0061] Step 114—Defining a filter to determine the market position.Using the CF series for all holding periods up to and including h as theonly input, define a mathematical filter, FL, with only two possibleoutcomes, plus or minus. The outcome computed in holding period h willdetermine the market position, PO, for the next holding period, h+1.Thus, if

FL(CF(0,m) . . . CF(h,m)) is positive, then PO(h+1,m)=1

Otherwise PO(h+1,m)=−1  (2)

[0062] The purpose of the Filter is to provide a rule for determiningprice trends, both up and down, in the market under consideration. Thereare, of course, many such rules, but a simple filter could be based on amoving average. For example, assuming the holding period is one calendarmonth, such a filter may be described as follow: if the current value ofCF is above the 12 month moving average of the CF series, then themarket position should be long for the following holding period,otherwise the position would be short. Other possible filters could usedifferent moving average lengths, different ways to determine whether aposition should be long or short (e.g., it should be long only if thecurrent value of CF is above the current value of the 12 month movingaverage by 10% or more), or completely different mechanisms such as themonthly changes in CF, or “runs” in the direction of these monthlychanges.

[0063] Step 116—Calculating the market return (R) of the asset classmember (m) in the holding period (h+1), denoted R(h+1,m).

R(h+1,m)=((PL(h+1,k,m)/PL(h,k,m))−1)*PO(h+1,m)  (3)

[0064] where PL(h,k,m) is the price of futures contract k of market m onthe last business day of holding period h.

[0065] Step 118—Calculating the class return. Assume that commercialasset class, C, can be represented by N members. Then the return for Cis the average of the holding period returns for each member in class C:

R(h+1,C)=Sum(R(h+1,m))/N for all m in C.  (4)

[0066] Calculating the class return in this fashion assumes equalweighting of the members of the asset class, with rebalancing occurringat the beginning of each holding period (i.e., each member begins eachnew period with the same value regardless of whether one had grown morerapidly than another in the prior period).

[0067] Step 118A—(Optional) Adding interest income. Since the return forany asset class C is calculated from the returns on representativefutures contracts for representative members of the class, and sincefutures market investors earn interest on their equity balances, it maybe appropriate to add T-bill interest to the asset class returncalculated in equation (4) as is done in the MLM Index™. Thus, the indexfor asset class C can be constructed to include interest income but neednot be so constructed.

[0068] Step 120—Creating a composite Index for asset class C in holdingperiod h+1, defined as MLMI(h−1,C) and determined by:

MLMI(h+1,C)−MLMI(h,C)*(1+R(h+1,C))  (5)

[0069] where MLMI(0,C)=1000.

II. A Specific Example: Constructing a Currency Index

[0070] Step 110—Set the following variables for the currency assetclass:

[0071] a. The members to be included are the British Pound, the JapaneseYen, the Swiss Franc, the Australian Dollar, the Canadian Dollar, andthe Euro.

[0072] b. For each member, the futures contracts traded on the IMMdivision of the Chicago Mercantile Exchange, with deliveries in March,June, September and December will be used to construct the Index

[0073] c . The holding period will be one calendar month.

[0074] d. The contract whose expiration month (also called the deliverymonth) follows most closely the holding period month will be used. Inthe currency markets, the contracts begin expiring in the first week ofthe delivery month, so, the March contract will be used in the Februaryholding period, the June contract will be used in the March holdingperiod, and so on.

[0075] Step 112—For each asset class member and its representativefutures contracts, construct a continuous futures series. Table I belowdemonstrates the calculation procedure for one member of the currencyclass, the Japanese Yen (JY). CF(h,JY) is calculated according toequation (1) above, with CF(0,JY) 1000.

[0076] Step 114—Using CF for all holding periods up to h as the onlyinput, define a filter FL that determines a position, PO, for the nextholding period as in equation (2) above. For this specific case,consider the function MA, the average of the most recent last 12 valuesof CF(h,JY):

MA(h,JY)=Sum(CF(h,JY) . . . CF(h−11,JY))/12.

[0077] Further, let the filter FL be described as follows:

FL(CF(0,JY) . . . CF(h,JY))=CF(h,JY)−MA(h,JY).  (6)

[0078] Then,

If CF(h,JY)−MA(h,JY) is positive then PO(h+1,JY)=1

otherwise PO(h+1,JY)=−1.

[0079] Table II shows the results of applying this filter to theJapanese Yen.

[0080] Step 116—Now that the market position has been determined,calculate the return R(h,JY) for the Japanese Yen in each holding periodh from equation (3) above. Specifically,

R(h+1,JY)=((PL(h+1,k,JY)/PL(h,k,JY))−1)*PO(h+1,JY).

[0081] Table III illustrates the calculation for the entire period usedin this example.

[0082] Note that the position PO for any period is determined at thebeginning of that period using data covering all periods through the endof the prior one. For example, the position to be held in the Marchholding period is determined using market prices up to the next to lastbusiness day of February. Thus, the return calculation does not use anydata that are not observable before the calculation takes place. As aresult, the Index returns can be replicated in real time.

[0083] Step 118—In the same manner, returns can be calculated for allmembers of a commercial asset class. Table IV (columns 2-8) provides thereturns for all the specified representative members of the currencyclass according to equation (3). The return of the class, calculatedaccording to equation (4), is the average of the returns of eachrepresentative member for each holding period, and is provided in column9. Such a calculation implies an equal weighting in the Class for eachmember.

[0084] Step 120—The Index for the Currency class MLMI(h,C) follows frominserting the series of class returns in equation (5) in order, afterestablishing an arbitrary starting value of 1000. This calculation isalso shown in Table IV columns 9-10.

[0085] Following the same straightforward procedures, class returns andindices can be created for any commercial asset class, such ascommodities or bonds.

III. Benchmarking Hedge Funds

[0086] Hedge funds are private investment funds, generally structured aslimited partnerships or limited liability companies, which can useleverage and charge performance-related fees to the investors. The fundscan only be offered to qualified investors. Their intended investmentactivities are enumerated in the offering documents and range fromtraditional investments such as stocks or bonds to various types ofarbitrage such as merger arbitrage or yield curve arbitrage. The fundsare often categorized according to their area of specialization. Forexample, “technology” funds specialize in investments in technologycompanies while “global macro” hedge funds are hedge funds whose rangeof potential investments is virtually unlimited.

[0087] Given these definitions, particularly the range of marketsemployed and the use of leverage, it is not surprising that attempts tobenchmark this type of investing have been unsatisfactory. Other indiceshave floundered on their inability to capture the returns of thecommercial asset classes included in these funds' portfolios. Themethodology of the preferred embodiment provides a broad solution tothis problem.

[0088] Following the methodology, a benchmark for hedge funds can becalculated by applying the procedures described above to the individualasset classes in which these funds invest, and combining the asset classindices using appropriate weights. Thus, the returns of hedge funds inholding period h can be defined as

R(h,I)=R(h,C(1))*W(C(1))+R(h,C(2))*W(C(2))+. . . +R(h,C(J))*W(C(J))  (7)

[0089] where R(h,C(j)) is the return of asset class C(j) in holdingperiod h,

[0090] W(C(j)) is the weight assigned to that asset class, and

[0091] J is the number of asset classes considered.

[0092] The asset classes can include classes such as equities, which canbe represented by the standard existing benchmarks, or commercial assetclasses, represented by the benchmarks created according to thepreferred embodiment.

[0093] The weights may be chosen with various goals in mind. Forexample, weights may be chosen to most closely fit the historical returnof some fund or manager. Alternatively, the weights may be chosen forsimplicity, such as having equal dollars invested in each asset class,with a given level of leverage. For example, to constrain leverage to200% when the index has four equally weighted asset classes, weights of0.5 would be applied to the returns of each asset class. Another goalfor choosing weights may be to have equal dollar volatility in eachasset class, for a given level of leverage. In a two-asset-class index,if asset class 1 has twice the volatility of asset class 2, and leverageis to be constrained to 150%, then the weight for asset class 1 would be0.5, and the weight for asset class 2 would be 1.

[0094] Referring to FIG. 2, a hedge fund index may be generated asfollows:

[0095] Step 210—Select asset classes, to be included in the index, andfor each commercial asset class select representative members of theclass and representative futures contracts for these members. Alsoselect the holding period.

[0096] Step 212—Compute return for each commercial class, as describedin steps 110 through 118.

[0097] Step 214—Optionally, select the stock market index to be used.

[0098] Step 216—Select weights for each class.

[0099] Step 218—Compute the fund return as the weighted average of classreturns, applying equation (7).

[0100] Step 220—Compute the fund index based on the weighted classreturns as follows:

MLMI(h+1,I)=MLMI(h,I)*(1+R(h+1,I))  (8)

[0101] where MLMI(0,I)−1000.

[0102] Table V shows one such index constructed by applying themethodology of the preferred embodiment to the following asset classes:US stocks represented by the S&P 500 Index, and global bonds, currenciesand commodities represented by indices produced by the methodology ofthe preferred embodiment. Following Sharpe, the weights in this examplewere chosen to minimize the squared differences between the monthlyreturns of the index and the returns of an index of hedge fund managers.The Index used for comparison in this example is the Hedge Fund Return(“HFR”) Index, a broad index of the returns of hedge fund managers thatis available commercially, but because it is just a compilationmanagers' actual returns, it cannot be traded in real time. The averageholding period return for R(h,I) over the 5 year period is 1.29%,compared to 1.26% for the HFR Index.

[0103] Similarly, the MLM GMS™, a particular application of thepreferred embodiment that has ben in commercial use for about sixmonths, includes U.S. stocks represented by the S&P 500, foreign stocksrepresented by the DAX, CAC, FTSE, and the Nikkei 225, and global bonds,currencies, and commodities represented by sub indices calculated in themanner described above.

[0104] The present invention is described in connection with a preferredembodiment but is defined without limitation by the appended claims andincludes insubstantial variations in elements and method steps. TABLE IHolding Next to last Market Contract Contract Period (h) Day (m) Month(k) Year PR (h, k, m) PR (h-1, k, m) CF (h, m) 1000 Feb-97 2/27/97 JYMarch 1997 0.8298 0.8275 1002.78 Mar-97 3/27/97 JY June 1997 0.81930.8405 977.49 Apr-97 4/29/97 JY June 1997 0.7937 0.8193 946.94 May-975/29/97 JY June 1997 0.8615 0.7937 1027.83 Jun-97 6/27/97 JY September1997 0.8827 0.873 1039.25 Jul-97 7/30/97 JY September 1997 0.8493 0.8827999.93 Aug-97 8/28/97 JY September 1997 8.8414 0.8493 990.63 Sep-979/29/97 JY December 1997 0.8353 0.8534 970.76 Oct-97 10/30/97 JYDecember 1997 0.836 0.8353 971.57 Nov-97 11/26/97 JY December 19970.7888 0.836 916.72 Dec-97 12/30/97 JY March 1998 0.7765 0.8002 889.56Jan-98 1/29/98 JY March 1998 0.8011 0.7765 917.75 Feb-98 2/26/98 JYMarch 1998 0.7886 0.8011 903.43 Mar-98 3/30/98 JY June 1998 0.76540.7986 865.87 Apr-98 4/29/98 JY June 1998 0.7606 0.7654 860.44 May-985/28/98 JY June 1998 0.7221 0.7606 816.89 Jun-98 6/29/98 JY September1998 0.7132 0.7316 796.34 Jul-98 7/30/98 JY September 1998 0.7001 0.7132781.71 Aug-98 8/28/98 JY September 1998 0.7067 0.7001 789.08 Sep-989/29/98 JY December 1998 0.753 0.7159 829.97 Oct-98 10/29/98 JY December1998 0.8603 0.753 948.24 Nov-98 11/27/98 JY December 1998 8.8141 0.8603897.32 Dec-98 12/30/98 JY March 1999 0.8789 0.8248 956.18 Jan-99 1/28/99JY March 1999 0.8639 0.8789 939.86 Feb-99 2/25/99 JY March 1999 0.83750.8639 911.14 Mar-99 3/30/99 JY June 1999 0.8396 0.8477 902.43 Apr-994/29/99 JY June 1999 0.8448 0.8396 908.02 May-99 5/27/99 JY June 19990.8324 0.8448 894.69 Jun-99 6/29/99 JY September 1999 0.8352 0.843886.41 Jul-99 7/29/99 JY September 1999 0.8717 0.8352 925.15 Aug-998/30/99 JY September 1999 0.9053 0.8717 960.81 Sep-99 9/29/99 JYDecember 1999 0.9452 0.9177 989.60 Oct-99 10/28/99 JY December 19990.9576 0.9452 1002.59 Nov-99 11/29/99 JY December 1999 0.9802 0.95761026.25 Dec-99 12/30/99 JY March 2000 0.9864 0.9948 1017.58 Jan-001/28/00 JY March 2000 0.9405 0.9864 970.23 Feb-00 2/28/00 JY March 20000.9173 0.9405 946.30

[0105] TABLE II Holding Next to last Market Contract Contract Period (h)Day (m) Month (k) Year PR (h, k, m) PR (h-1, k, m) CF (h, m) MA (h, m)PO (h, m) 1000 Feb-97 2/27/97 JY March 1997 0.8298 0.8275 1002.78 Mar-973/27/97 JY June 1997 0.8193 0.8405 977.49 Apr-97 4/29/97 JY June 19970.7937 0.8193 946.94 May-97 5/29/97 JY June 1997 0.8615 0.7937 1027.83Jun-97 6/27/97 JY September 1997 0.8827 0.873 1039.25 Jul-97 7/30/97 JYSeptember 1997 0.8493 0.8827 999.93 Aug-97 8/28/97 JY September 19978.8414 0.8493 990.63 Sep-97 9/29/97 JY December 1997 0.8353 0.8524970.76 Oct-97 10/30/97 JY December 1997 0.836 0.8353 971.57 Nov-9711/26/97 JY December 1997 0.7888 0.836 916.72 Dec-97 12/30/97 JY March1998 0.7765 0.8002 889.56 977.79 Jan-98 1/29/98 JY March 1998 0.80110.7765 917.75 970.93 −1 Feb-98 2/26/98 JY March 1998 0.7886 0.8011903.43 962.65 −1 Mar-98 3/30/98 JY June 1998 0.7654 0.7986 865.87 953.35−1 Apr-98 4/29/98 JY June 1998 0.7606 0.7654 860.44 946.14 −1 May-985/28/98 JY June 1998 0.7221 0.7606 816.89 928.57 −1 Jun-98 6/29/98 JYSeptember 1998 0.7132 0.7316 796.34 908.32 −1 Jul-98 7/30/98 JYSeptember 1998 0.7001 0.7132 781.71 890.14 −1 Aug-98 8/28/98 JYSeptember 1998 0.7067 0.7001 789.08 873.34 −1 Sep-98 9/29/98 JY December1998 0.753 0.7159 829.97 861.61 −1 Oct-98 10/29/98 JY December 19980.8603 0.753 948.24 859.67 −1 Nov-98 11/27/98 JY December 1998 8.81410.8603 897.32 858.05 1 Dec-98 12/30/98 JY March 1999 0.8789 0.8248956.18 863.60 1 Jan-99 1/28/99 JY March 1999 0.8639 0.8789 939.86 865.441 Feb-99 2/25/99 JY March 1999 0.8375 0.8639 911.14 866.09 1 Mar-993/30/99 JY June 1999 0.8396 0.8477 902.43 869.13 1 Apr-99 4/29/99 JYJune 1999 0.8448 0.8396 908.02 873.10 1 May-99 5/27/99 JY June 19990.8324 0.8448 894.69 879.58 1 Jun-99 6/29/99 JY September 1999 0.83520.843 886.41 887.09 1 Jul-99 7/29/99 JY September 1999 0.8717 0.8352925.15 899.04 −1 Aug-99 8/30/99 JY September 1999 0.9053 0.8717 960.81913.35 1 Sep-99 9/29/99 JY December 1999 0.9452 0.9177 989.60 926.66 1Oct-99 10/28/99 JY December 1999 0.9576 0.9452 1002.59 931.18 1 Nov-9911/29/99 JY December 1999 0.9802 0.9576 1026.25 941.93 1 Dec-99 12/30/99JY March 2000 0.9864 0.9948 1017.58 947.05 1 Jan-00 1/28/00 JY March2000 0.9405 0.9864 970.23 949.58 1 Feb-00 2/28/00 JY March 2000 0.91730.9405 946.30 952.51 1 Mar-00 −1

[0106] TABLE III Last day of Holding holding Market Contract Contract PLPL Period (h) period (m) Month (k) Year (h, m) (h-1, m) PO (h, m) R (h,m) Jan-98 1/29/98 JY March 1998 0.7915 0.7736 −1 −2.31% Feb-98 2/26/98JY March 1998 0.7948 0.7915 −1 −0.42% Mar-98 3/30/98 JY June 1998 0.75870.805 −1 5.75% Apr-98 4/29/98 JY June 1998 0.7567 0.7587 −1 0.26% May-985/28/98 JY June 1998 0.7222 0.7567 −1 4.56% Jun-98 6/29/98 JY September1998 0.7266 0.7317 −1 0.70% Jul-98 7/30/98 JY September 1998 0.69540.7266 −1 4.29% Aug-98 8/28/98 JY September 1998 0.7107 0.6954 −1 −2.20%Sep-98 9/29/98 JY December 1998 0.739 0.72 −1 −2.64% Oct-98 10/29/98 JYDecember 1998 0.8671 0.739 −1 −17.33% Nov-98 11/27/98 JY December 19980.8134 0.8671   1 −6.19% Dec-98 12/30/98 JY March 1999 0.8884 0.8241   17.80% Jan-99 1/28/99 JY March 1999 0.8637 0.8884   1 −2.78% Feb-992/25/99 JY March 1999 0.8416 0.8637   1 −2.56% Mar-99 3/30/99 JY June1999 0.8486 0.8519   1 −0.39% Apr-99 4/29/99 JY June 1999 0.842 0.8486  1 −0.78% May-99 5/27/99 JY June 1999 0.825 0.842   1 −2.02% Jun-996/29/99 JY September 1999 0.8348 0.8356   1 −0.10% Jul-99 7/29/99 JYSeptember 1999 0.8787 0.8348 −1 −5.26% Aug-99 8/30/99 JY September 19990.9147 0.8787   1 4.10% Sep-99 9/29/99 JY December 1999 0.95 0.9273   12.45% Oct-99 10/28/99 JY December 1999 0.9659 0.95   1 1.67% Nov-9911/29/99 JY December 1999 0.9833 0.9659   1 1.80% Dec-99 12/30/99 JYMarch 2000 0.9892 0.9979   1 −0.87% Jan-00 1/28/00 JY March 2000 0.93680.9892   1 −5.30% Feb-00 2/28/00 JY March 2000 0.91 0.9368   1 −2.86%Mar-00 −1

[0107] TABLE IV Currency Holding Market Returns Class Period DA BP CA DMEU JY SF Return MLMI (h, C) 1000 Jan-98 −5.43% −1.08% 1.90% 1.88% na−2.31% 1.28% −0.63% 993.71 Feb-98 1.08% 0.92% −2.25% −0.64% na −0.42%−0.50% −0.30% 990.71 Mar-98 2.60% 1.78% −0.28% 1.95% na 5.75% 4.39%2.70% 1017.45 Apr-98 1.97% 0.19% 0.92% −2.71% na 0.26% −1.15% −0.08%1016.60 May-98 4.11% −2.31% 1.83% −0.43% na 4.56% −0.90% 1.14% 1028.21Jun-98 0.94% −2.30% 0.83% 1.42% na 0.70% 2.83% 0.74% 1035.78 Jul-982.00% −1.97% 2.98% −1.49% na 4.29% −1.67% 0.69% 1042.91 Aug-98 5.79%−3.06% 3.90% −1.03% na −2.20% −3.00% 0.07% 1043.60 Sep-98 −4.24% 1.34%−2.88% 5.03% na −2.64% 4.22% 0.14% 1045.06 Oct-98 −4.90% −1.31% 0.90%0.72% na −17.33% 1.75% −3.36% 1009.91 Nov-98 −0.43% −1.40% −0.65% −2.48%na −6.19% −3.52% −2.45% 985.21 Dec-98 −3.14% 0.95% 0.08% 1.50% na 7.80%1.30% 1.42% 999.16 Jan-99 −3.58% −0.71% −1.53% −3.24% na −2.78% −3.11%−2.49% 974.25 Feb-99 −1.94% −2.66% −0.02% −3.19% na −2.56% −2.55% −2.15%953.28 Mar-99 2.73% −0.66% −0.11% 2.52% na −0.39% 2.79% 1.15% 964.23Apr-99 4.19% 0.14% −3.45% 1.76% na −0.78% 2.84% 0.78% 971.76 May-99−1.50% 0.47% −1.06% 1.90% na −2.02% 0.80% −0.23% 969.49 Jun-99 2.10%1.34% 0.60% 0.71% na −0.10% 1.56% 1.04% 979.53 Jul-99 −2.13% −2.75%−2.65% −3.25% na −5.26% −3.63% −3.28% 947.42 Aug-99 −1.81% 0.97% 0.71%0.00% na 4.10% 1.87% 0.97% 956.63 Sep-99 1.93% −2.38% 1.74% 0.00% na2.45% −0.60% 0.53% 961.66 Oct-99 −2.54% −0.10% −0.31% 0.00% na 1.67%1.92% 0.11% 962.69 Nov-99 −0.03% −2.78% −0.22% 0.00% na 1.80% 4.54%0.55% 968.00 Dec-99 −3.23% −1.20% 1.70% 0.00% na −0.87% 0.36% −0.54%962.78 Jan-00 −3.51% −0.21% −0.12% na 4.28% −5.30% 4.58% −0.04% 962.35Feb-00 2.57% −2.24% −0.26% na 0.54% −2.86% 0.48% −0.30% 959.51

[0108] TABLE V Asset Classes Returns Weights 1.030 0.468 LeverageHolding Global 0.205 0.117 US 182.05% Indexes Period Bonds CurrencyCommodity Stocks R (h, I) HFR MLMI HFR 1000 1000 Jan-1995 −1.22% 0.31%1.50% 2.35% 0.08% −0.87% 1000.8426 991.3 Feb-1995 −0.38% 1.26% −0.29%3.46% 1.45% 1.45% 1015.3633 1005.6739 Mar-1995 1.15% 4.86% −3.93% 2.41%2.84% 1.40% 1044.2332 1019.7533 Apr-1995 1.09% −0.43% 0.69% 2.45% 2.27%1.78% 1067.8974 1027.7074 May-1995 3.46% −0.92% 0.73% 3.21% 4.97% 2.54%1120.9221 1053.8111 Jun-1995 −1.10% 0.60% 3.07% 1.72% 0.16% 0.47%1122.7441 1058.764 Jul-1995 0.58% −1.35% −1.22% 2.92% 1.55% 3.93%1140.1206 1100.3735 Aug-1995 1.34% −3.20% 0.10% −0.05% −0.32% 5.59%1136.4766 1161.8843 Sep-1995 0.81% −0.77% 3.54% 3.66% 2.80% 3.22%1168.3165 1199.297 Oct-1995 1.15% 1.01% 0.15% −0.74% 1.06% 0.41%1180.7199 1204.2141 Nov-1995 2.08% −1.10% −0.39% 4.01% 3.74% 3.63%1224.9074 1247.9271 Dec-1995 0.65% 0.17% 2.62% 0.93% 1.45% 3.63%1242.6406 1293.2269 Jan-1996 0.55% 0.79% −1.75% 3.15% 2.00% 5.28%1267.5312 1361.5092 Feb-1996 −2.45% −0.31% 1.50% 0.05% −2.39% −3.77%1237.2042 1310.1803 Mar-1996 −0.27% 0.62% 2.37% 1.09% 0.64% 0.37%1245.0615 1315.028 Apr-1996 0.40% 1.39% 1.38% 0.55% 1.12% 3.11%1258.9734 1355.9254 May-1996 −0.42% 0.31% −1.39% 1.85% 0.33% −0.08%1263.1677 1354.8406 Jun-1996 −0.48% 0.08% 3.40% 0.60% 0.20% −1.06%1265.6904 1340.4793 Jul-1996 0.03% −2.02% −2.99% −5.08% −3.12% −3.04%1226.2263 1299.7288 Aug-1996 0.79% 1.03% 1.31% 1.39% 1.83% 0.73%1248.7226 1309.2168 Sep-1996 1.35% 1.70% 1.74% 5.20% 4.38% 2.01%1303.3726 1335.532 Oct-1996 0.70% 1.66% −1.10% 2.64% 2.17% 1.58%1331.6528 1356.6334 Nov-1996 2.08% 1.66% 0.09% 6.85% 5.70% 4.72%1407.6115 1420.6665 Dec-1996 −1.15% 0.56% 2.43% −2.72% −2.06% −0.49%1378.6015 1413.7053 Jan-1997 0.98% 0.87% −0.56% 5.78% 3.83% 5.14%1431.4283 1486.3697 Feb-1997 0.77% 0.76% −2.51% 0.37% 0.82% 1.59%1443.1894 1510.003 Mar-1997 −1.67% 0.35% 1.70% −5.00% −3.79% −1.24%1388.4266 1491.279 Apr-1997 0.24% 1.50% 2.13% 5.91% 3.58% −0.22%1438.0788 1487.9982 May-1997 −0.01% −1.91% 3.11% 5.95% 2.76% 1.83%1477.704 1515.2285 Jun-1997 1.03% 1.33% −2.88% 3.60% 2.69% 1.82%1517.3842 1542.8057 Jul-1997 1.78% 2.15% −0.12% 7.60% 5.82% 5.90%1605.7186 1633.8312 Aug-1997 −0.69% 0.23% 0.90% −5.73% −3.24% −1.25%1553.619 1613.4083 Sep-1997 2.04% −0.72% −2.75% 4.57% 3.77% 3.05%1612.1464 1662.6173 Oct-1997 0.32% −0.47% 0.51% −3.20% −1.20% −1.60%1592.7203 1636.0154 Nov-1997 0.53% 1.90% −0.73% 3.34% 2.42% −0.25%1641.2073 1631.9254 Dec-1997 0.91% 1.79% 1.83% 1.43% 2.19% 2.93%1666.9956 1679.7408 1000 1000 Jan-1998 1.37% −0.63% −0.28% 0.89% 1.67%0.20% 1694.8042 1683.1003 Feb-1998 0.22% −0.30% 4.06% 6.35% 3.61% 1.90%1756.0489 1715.0792 Mar-1998 0.43% 2.70% 0.88% 4.59% 3.25% 5.05%1813.1435 1801.6907 Apr-1998 0.09% −0.08% 1.51% 0.78% 0.62% −0.13%1824.3857 1799.3485 May-1998 1.05% 1.14% 0.62% −2.54% 0.20% 0.08%1827.9934 1800.7879 Jun-1998 0.08% 0.74% −0.13% 3.65% 1.93% 0.57%1863.3577 1811.0524 Jul-1998 0.48% 0.69% 1.19% −1.75% −0.04% 0.23%1862.5305 1815.2179 Aug-1998 2.94% 0.07% 7.06% −15.05% −3.18% −3.70%1803.326 1748.0548 Sep-1998 2.69% 0.14% −5.79% 6.45% 5.14% −0.50%1896.0963 1739.3145 Oct-1998 −1.14% −3.36% 0.95% 7.72% 1.87% −1.83%1931.4688 1707.4851 Nov-1998 0.67% −2.45% 4.07% 5.18% 3.09% 1.98%1991.2077 1741.2933 Dec-1998 −0.49% 1.42% 2.09% 6.08% 2.87% 2.44%2048.4359 1783.7808 Jan-1999 0.66% −2.49% 2.73% 2.89% 1.85% 0.81%2086.2349 1798.2294 Feb-1999 −2.78% −2.15% 5.23% −3.59% −4.37% −1.24%1995.0637 1775.9314 Mar-1999 −0.12% 1.15% −10.63% 3.67% 0.58% 1.07%2006.7298 1794.9339 Apr-1999 0.59% 0.78% 1.23% 3.34% 2.48% 3.86%2056.4838 1864.2183 May-1999 −0.43% −0.23% −1.71% −2.94% −2.07% −0.90%2013.9812 1847.4403 Jun-1999 −1.49% 1.04% −2.04% 5.47% 1.00% 2.16%2034.0273 1887.3451 Jul-1999 1.11% −3.28% 3.86% −3.61% −0.77% 0.46%2018.4081 1896.0268 Aug-1999 0.08% 0.97% −0.76% −0.90% −0.23% −0.55%2013.7325 1885.5987 Sep-1999 0.41% 0.53% 0.72% −2.76% −0.68% 1.08%2000.1236 1905.9632 Oct-1999 −0.34% 0.11% −2.72% 6.01% 2.16% −0.85%2043.371 1889.7625 Nov-1999 −0.41% 0.55% −1.75% 1.11% 0.01% 3.59%2043.5886 1957.605 Dec-1999 1.66% −0.54% 0.68% 5.30% 4.16% 6.66%2128.6503 2087.9814

What is claimed is:
 1. A method for generating an index of investment returns comprising the steps of: (a) selecting a representative set of assets, where said assets may be grouped into a plurality of classes; (b) generating a rule to determine a position for each of said assets for time t; (c) determining the position for each of said assets for said time t; (d) determining a market price for each of said assets for said time t; (e) computing a return for each of said assets for said time t, said return being a function of the position and the market price determined in steps (c) and (d); (f) averaging the returns computed in step (e) for all the selected assets in each of said plurality of classes, the average for each of said classes is the return for that class; and (g) computing the index as a function of the returns for each class:
 2. The method of claim 1, where the step (g) of computing the index further comprises the steps of selecting weights such that each weight corresponds to one of said plurality of classes, and averaging the products of the return for each class multiplied by its corresponding weight.
 3. A method for generating a series of investment returns with respect to time, the method comprising the steps of: (a) selecting a plurality of assets from a plurality of asset classes; (b) determining a position for each of said assets for a time t; (c) determining a market price for each of said assets for said time t; (d) computing an asset return for each of said assets for said time t, said asset return being a function of the position and the market price; (e) averaging said asset returns computed in step (d) for said time t, for all of said assets in each of said asset classes, to determine a class return for each of said asset classes; and (f) computing an investment return for said time t, in the series of investment returns, as a second function of the class returns for each of said asset classes for said time t.
 4. The method of claim 3, wherein the step of computing the investment return further comprises the steps of selecting weights such that each weight corresponds to one of said asset classes, and averaging the products of the class return for each asset class multiplied by the corresponding weight.
 5. The method of claim 3, further comprising selecting at least one asset from each of two commercial markets.
 6. The method of claim 3, wherein said plurality of asset classes comprises at least one from the group of: commodities, currencies, and bonds.
 7. The method of claim 3, further comprising determining said position based on whether the market price for each of said assets increased or decreased since a predefined time preceding said time t.
 8. The method of claim 3, further comprising determining said position based on a moving average of the asset returns for each of said assets for a predetermined past time period.
 9. The method of claim 3, further comprising the steps of: (a) determining a continuous series of returns for each of said assets, wherein a return is determined using a futures contract for each of said assets for each of a plurality o f holding periods; (b) determining an average of returns of the asset based on the continuous series over a predetermined number of past holding periods; and (c) determining said position as a function of the return for a current holding period according to said continuous series and said average of returns.
 10. The method of claim 9, further comprising the steps of setting the position to long when the return for the current holding period according to said continuous series is greater than the average of returns, and otherwise setting the position to short.
 11. The method of claim 3, further comprising the steps of determining one or more futures contracts for each of said assets, for said time t, and determining the market price for each of said assets for said time t in accordance with the futures contract for said time t.
 12. The method of claim 3, wherein said step of computing the asset return for each of said assets further comprises the step of setting the asset return equal to the product of the market price at said time t divided by the market price at a preceding time t−1 multiplied by the position for said time t.
 13. The method of claim 3, further comprising the steps of determining the investment return for time t as the average of the class returns for time t, and determining an index for time t as the product of the index for a preceding time t−1 multiplied by the sum of one plus the investment return for time t.
 14. A method for generating a series of investment returns with respect to time, the method comprising the steps of: (a) selecting a plurality of assets from a plurality of asset classes wherein said plurality of asset classes includes at least one from the group of commodities, currencies, and bonds; (b) determining a market price for each of said assets for said time t; (c) determining a trend in asset value for each of said assets over a predefined past period; (d) computing an asset return for each of said assets for said time t in accordance with a function of the market price and the trend; (e) computing a class return for each of said plurality of asset classes for said time t based on an average of said asset returns; and (f) computing an investment return for said time t in the series of investment returns, as a second function of the class returns for each of said asset classes.
 15. The method of claim 14, wherein the step of computing the investment return further comprises the steps of selecting weights such that each weight corresponds to one of said plurality of asset classes, and averaging the products of the class return for each asset class multiplied by the corresponding weight.
 16. A method for generating a series of investment returns for a plurality of asset classes, each class having at least one asset member, the method comprising the steps of: (a) determining a plurality of holding periods; (b) determining a futures contact for each asset member, each futures contract having a market price for each of said holding periods; (c) calculating a continuous future series of returns for each asset member based on the futures contract and the market price for said asset member for each of said holding periods; (d) determining a position for each said asset member for each of said holding periods based on said continuous future series for the preceding holding periods; (e) calculating an asset return for each said asset member based on the market price and the position; (f) calculating a class return for each asset class based on the market returns for each asset member in said class; and (g) calculating an investment return for said holding period in the series of investment returns, based on the class returns.
 17. The method of claim 16, wherein said plurality of asset classes comprises at least one from the group of: commodities, currencies, and bonds.
 18. A method for generating a series of investment returns for a plurality of asset classes, each class having at least one asset member, the method comprising the steps of: (a) receiving a holding period for each said asset member; (b) determining a futures contact for each asset member, each said futures contract having a market price for each said holding period; (c) determining a position for each said asset member based on the futures contract, the market price and the holding period; (d) determining an asset return for each said asset member as a function of the position; (e) determining a class return for each asset class as an average of the asset return for each said asset member; (f) determining a weight corresponding to each said asset class; (g) determining a weighted return for each said asset class as a product of the class return for each said asset class and the corresponding weight; and (h) determining an investment return for said holding period as a sum of the weighted return for each said asset class.
 19. The method of claim 18, wherein said plurality of asset classes comprises at least one from the group of commodities, currencies, and bonds.
 20. A method for generating an index of investment returns comprising the steps of: (a) selecting a representative set of asset members from a plurality of asset classes, wherein said plurality of asset classes includes at least one from the group of commodities, currencies, and bonds; (b) receiving market data relating to each of said selected asset members; (c) computing a return for each of said asset classes based on said market data; (d) generating a weight for each of said asset classes; and (e) computing the index as a function of the products of the return for each of said asset classes and the corresponding weight.
 21. The method of claim 20, wherein the step of generating said weight further comprises the step of setting the weight as a function of the percentage of asset members in each of said asset classes.
 22. A system for generating an index of investment returns, comprising a processor; and a memory storing processing instructions for controlling the processor, the processor operative with the processing instructions for: (a) selecting a plurality of assets from a plurality of asset classes; (b) determining a position for each of said assets for a time t; (c) determining a market price for each of said assets for said time t; (d) computing an asset return for each of said assets for said time t, said asset return being a function of the position and the market price; (e) averaging said asset returns computed in step (d) for said time t, for all of said assets in each of said asset classes, to determine a class return for each of said asset classes; and (f) computing an investment return for said time t, in the series of investment returns, as a second function of the class returns for each of said asset classes for said time t.
 23. The system of claim 22, wherein the step of computing the investment return further comprises the steps of selecting weights such that each weight corresponds to one of said asset classes, and averaging the products of the class return for each asset class multiplied by the corresponding weight.
 24. A system for generating an index of investment returns, comprising a processor; and a memory storing processing instructions for controlling the processor, the processor operative with the processing instructions for: (a) selecting a plurality of assets from a plurality of asset classes wherein said plurality of asset classes includes at least one from the group of commodities, currencies, and bonds; (b) determining a market price for each of said assets for said time t; (c) determining a trend in asset value for each of said assets over a predefined past period; (d) computing an asset return for each of said assets for said time t in accordance with a function of the market price and the trend; (e) computing a class return for each of said plurality of asset classes for said time t based on an average of said asset returns; and (f) computing an investment return for said time t in the series of investment returns, as a second function of the class returns for each of said asset classes.
 25. A system for generating an index of investment returns, comprising a processor; and a memory storing processing instructions for controlling the processor, the processor operative with the processing instructions for: (a) determining a plurality of holding periods; (b) determining a futures contact for each asset member, each futures contract having a market price for each of said holding periods; (c) calculating a continuous future series of returns for each asset member based on the futures contract and the market price for said asset member for each of said holding periods; (d) determining a position for each said asset member for each of said holding periods based on said continuous future series for the preceding holding periods; (e) calculating an asset return for each said asset member based on the market price and the position; (f) calculating a class return for each asset class based on the market returns for each asset member in said class; and (g) calculating an investment return for said holding period in the series of investment returns, based on the class returns.
 26. A system for generating an index of investment returns, comprising a processor; and a memory storing processing instructions for controlling the processor, the processor operative with the processing instructions for: (a) receiving a holding period for each said asset member; (b) determining a futures contact for each asset member, each said futures contract having a market price for each said holding period; (c) determining a position for each said asset member based on the futures contract, the market price and the holding period; (d) determining an asset return for each said asset member as a function of the position; (e) determining a class return for each asset class as an average of the asset return for each said asset member; (f) determining a weight corresponding to each said asset class; (g) determining a weighted return for each said asset class as a product of the class return for each said asset class and the corresponding weight; and (h) determining an investment return for said holding period as a sum of the weighted return for each said asset class.
 27. A system for generating an index of investment returns, comprising a processor; and a memory storing processing instructions for controlling the processor, the processor operative with the processing instructions for: (a) selecting a representative set of asset members from a plurality of asset classes, wherein said plurality of asset classes includes at least one from the group of commodities, currencies, and bonds; (b) receiving market data relating to each of said selected asset members; (c) computing a return for each of said asset classes based on said market data; (d) generating a weight for each of said asset classes; and (e) computing the index as a function of the products of the return for each of said asset classes and the corresponding weight.
 28. A computer-readable medium encoded with processing instructions for implementing a method for generating an index of investment returns, the method comprising: (a) selecting a plurality of assets from a plurality of asset classes; (b) determining a position for each of said assets for a time t; (c) determining a market price for each of said assets for said time t; (d) computing an asset return for each of said assets for said time t, said asset return being a function of the position and the market price; (e) averaging said asset returns computed in step (d) for said time t, for all of said assets in each of said asset classes, to determine a class return for each of said asset classes; and (f) computing an investment return for said time t, in the series of investment returns, as a second function of the class returns for each of said asset classes for said time t.
 29. The computer-readable medium of claim 28, wherein said step of computing the investment return further comprises the steps of selecting weights such that each weight corresponds to one of said asset classes, and averaging the products of the return for each asset class multiplied by its corresponding weight.
 30. A computer-readable medium encoded with processing instructions for implementing a method for generating an index of investment returns, the method comprising: (a) selecting a plurality of assets from a plurality of asset classes wherein said plurality of asset classes includes at least one from the group of commodities, currencies, and bonds; (b) determining a market price for each of said assets for said time t; (c) determining a trend in asset value for each of said assets over a predefined past period; (d) computing an asset return for each of said assets for said time t in accordance with a function of the market price and the trend; (e) computing a class return for each of said plurality of asset classes for said time t based on an average of said asset returns; and (f) computing an investment return for said time t in the series of investment returns, as a second function of the class returns for each of said asset classes.
 31. A computer-readable medium encoded with processing instructions for implementing a method for generating an index of investment returns, the method comprising: (a) determining a plurality of holding periods; (b) determining a futures contact for each asset member, each futures contract having a market price for each of said holding periods; (c) calculating a continuous future series of returns for each asset member based on the futures contract and the market price for said asset member for each of said holding periods; (d) determining a position for each said asset member for each of said holding periods based on said continuous future series for the preceding holding periods; (e) calculating an asset return for each said asset member based on the market price and the position; (f) calculating a class return for each asset class based on the market returns for each asset member in said class; and (g) calculating an investment return for said holding period in the series of investment returns, based on the class returns.
 32. A computer-readable medium encoded with processing instructions for implementing a method for generating an index of investment returns, the method comprising: (a) receiving a holding period for each said asset member; (b) determining a futures contact for each asset member, each said futures contract having a market price for each said holding period; (c) determining a position for each said asset member based on the futures contract, the market price and the holding period; (d) determining an asset return for each said asset member as a function of the position; (e) determining a class return for each asset class as an average of the asset return for each said asset member; (f) determining a weight corresponding to each said asset class; (g) determining a weighted return for each said asset class as a product of the class return for each said asset class and the corresponding weight; and (h) determining an investment return for said holding period as a sum of the weighted return for each said asset class.
 33. A computer-readable medium encoded with processing instructions for implementing a method for generating an index of investment returns, the method comprising: (a) selecting a representative set of asset members from a plurality of asset classes, wherein said plurality of asset classes includes at least one from the group of commodities, currencies, and bonds; (b) receiving market data relating to each of said selected asset members; (c) computing a return for each of said asset classes based on said market data; (d) generating a weight for each of said asset classes; and (e) computing the index as a function of the products of the return for each of said asset classes and the corresponding weight. 